In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.
The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..
"""
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"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Play around with view_sentence_range to view different parts of the data.
view_sentence_range = (0, 10)
"""
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"""
import numpy as np
print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))
scenes = text.split('\n\n')
print('Number of scenes: {}'.format(len(scenes)))
sentence_count_scene = [scene.count('\n') for scene in scenes]
print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))
sentences = [sentence for scene in scenes for sentence in scene.split('\n')]
print('Number of lines: {}'.format(len(sentences)))
word_count_sentence = [len(sentence.split()) for sentence in sentences]
print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))
print()
print('The sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
Dataset Stats Roughly the number of unique words: 11492 Number of scenes: 262 Average number of sentences in each scene: 15.248091603053435 Number of lines: 4257 Average number of words in each line: 11.50434578341555 The sentences 0 to 10: Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink. Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch. Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately? Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick. Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self. Homer_Simpson: I got my problems, Moe. Give me another one. Moe_Szyslak: Homer, hey, you should not drink to forget your problems. Barney_Gumble: Yeah, you should only drink to enhance your social skills.
The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:
To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:
vocab_to_intint_to_vocabReturn these dictionaries in the following tuple (vocab_to_int, int_to_vocab)
import numpy as np
import problem_unittests as tests
from collections import Counter
def create_lookup_tables(text):
"""
Create lookup tables for vocabulary
:param text: The text of tv scripts split into words
:return: A tuple of dicts (vocab_to_int, int_to_vocab)
"""
# TODO: Implement Function
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
return vocab_to_int, int_to_vocab
"""
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"""
tests.test_create_lookup_tables(create_lookup_tables)
Tests Passed
We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word "bye" and "bye!".
Implement the function token_lookup to return a dict that will be used to tokenize symbols like "!" into "||Exclamation_Mark||". Create a dictionary for the following symbols where the symbol is the key and value is the token:
This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token "dash", try using something like "||dash||".
def token_lookup():
"""
Generate a dict to turn punctuation into a token.
:return: Tokenize dictionary where the key is the punctuation and the value is the token
"""
# TODO: Implement Function
return {'.':'<PERIOD>',',':'<COMMA>','"':'<QUOTATION_MARK>',';':'<SEMICOLON>','!':'<EXCLAMATION_MARK>','?':'<QUESTION_MARK>','(':'<LEFT_PAREN>',')':'<RIGHT_PAREN>','--':'<DASH>','\n':'<RETURN>'}
"""
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"""
tests.test_tokenize(token_lookup)
Tests Passed
Running the code cell below will preprocess all the data and save it to file.
"""
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"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
"""
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"""
import helper
import numpy as np
import problem_unittests as tests
int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
"""
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"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
tf.__version__
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0 Default GPU Device: /gpu:0
Implement the get_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders:
name parameter.Return the placeholders in the following the tuple (Input, Targets, LearingRate)
def get_inputs():
"""
Create TF Placeholders for input, targets, and learning rate.
:return: Tuple (input, targets, learning rate)
"""
# TODO: Implement Function
Input = tf.placeholder(tf.int32, [None, None], name = 'input')
Targets = tf.placeholder(tf.int32, [None, None], name = 'targets')
LearningRate = tf.placeholder (tf.float32, name = 'learning_rate')
return (Input, Targets, LearningRate)
"""
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"""
tests.test_get_inputs(get_inputs)
Tests Passed
Stack one or more BasicLSTMCells in a MultiRNNCell.
rnn_sizezero_state() functiontf.identity()Return the cell and initial state in the following tuple (Cell, InitialState)
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
:param batch_size: Size of batches
:param rnn_size: Size of RNNs
:return: Tuple (cell, initialize state)
"""
num_of_lstm_layers = 4
keep_prob = 0.5
cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)
drop = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)
cell = tf.contrib.rnn.MultiRNNCell([drop] * num_of_lstm_layers)
initial_state = tf.identity(cell.zero_state(batch_size, tf.float32), name = 'initial_state')
return (cell, initial_state)
"""
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"""
tests.test_get_init_cell(get_init_cell)
Tests Passed
Apply embedding to input_data using TensorFlow. Return the embedded sequence.
def get_embed(input_data, vocab_size, embed_dim):
"""
Create embedding for <input_data>.
:param input_data: TF placeholder for text input.
:param vocab_size: Number of words in vocabulary.
:param embed_dim: Number of embedding dimensions
:return: Embedded input.
"""
# TODO: Implement Function
embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
embed = tf.nn.embedding_lookup(embedding, input_data)
return embed
"""
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"""
tests.test_get_embed(get_embed)
Tests Passed
You created a RNN Cell in the get_init_cell() function. Time to use the cell to create a RNN.
tf.nn.dynamic_rnn()tf.identity()Return the outputs and final_state state in the following tuple (Outputs, FinalState)
def build_rnn(cell, inputs):
"""
Create a RNN using a RNN Cell
:param cell: RNN Cell
:param inputs: Input text data
:return: Tuple (Outputs, Final State)
"""
# TODO: Implement Function
outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
final_state = tf.identity(final_state, name='final_state')
return outputs, final_state
"""
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"""
tests.test_build_rnn(build_rnn)
Tests Passed
Apply the functions you implemented above to:
input_data using your get_embed(input_data, vocab_size, embed_dim) function.cell and your build_rnn(cell, inputs) function.vocab_size as the number of outputs.Return the logits and final state in the following tuple (Logits, FinalState)
def build_nn(cell, rnn_size, input_data, vocab_size):
"""
Build part of the neural network
:param cell: RNN cell
:param rnn_size: Size of rnns
:param input_data: Input data
:param vocab_size: Vocabulary size
:return: Tuple (Logits, FinalState)
"""
# TODO: Implement Function
embed = get_embed(input_data, vocab_size, rnn_size)
outputs, final_state = build_rnn(cell, embed)
logits = tf.contrib.layers.fully_connected(outputs, vocab_size,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
biases_initializer=tf.zeros_initializer(),
activation_fn=None)
return (logits, final_state)
"""
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"""
tests.test_build_nn(build_nn)
Tests Passed
Implement get_batches to create batches of input and targets using int_text. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length). Each batch contains two elements:
[batch size, sequence length][batch size, sequence length]If you can't fill the last batch with enough data, drop the last batch.
For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3) would return a Numpy array of the following:
[
# First Batch
[
# Batch of Input
[[ 1 2 3], [ 7 8 9]],
# Batch of targets
[[ 2 3 4], [ 8 9 10]]
],
# Second Batch
[
# Batch of Input
[[ 4 5 6], [10 11 12]],
# Batch of targets
[[ 5 6 7], [11 12 13]]
]
]def get_batches(int_text, batch_size, seq_length):
"""
Return batches of input and target
:param int_text: Text with the words replaced by their ids
:param batch_size: The size of batch
:param seq_length: The length of sequence
:return: Batches as a Numpy array
"""
# TODO: Implement Function
n_batches = int(len(int_text) / (batch_size * seq_length))
xdata = np.array(int_text[: n_batches * batch_size * seq_length])
ydata = np.array(int_text[1: n_batches * batch_size * seq_length + 1])
x_batches = np.split(xdata.reshape(batch_size, -1), n_batches, 1)
y_batches = np.split(ydata.reshape(batch_size, -1), n_batches, 1)
return np.array(list(zip(x_batches, y_batches)))
"""
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"""
tests.test_get_batches(get_batches)
Tests Passed
Tune the following parameters:
num_epochs to the number of epochs.batch_size to the batch size.rnn_size to the size of the RNNs.seq_length to the length of sequence.learning_rate to the learning rate.show_every_n_batches to the number of batches the neural network should print progress.# Number of Epochs
num_epochs = 300
# Batch Size
batch_size = 256
# RNN Size
rnn_size = 704
# Sequence Length
seq_length = 11
# Learning Rate
learning_rate = 0.009
# Show stats for every n number of batches
show_every_n_batches = 100
"""
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"""
save_dir = './save'
Build the graph using the neural network you implemented.
"""
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"""
from tensorflow.contrib import seq2seq
train_graph = tf.Graph()
with train_graph.as_default():
vocab_size = len(int_to_vocab)
input_text, targets, lr = get_inputs()
input_data_shape = tf.shape(input_text)
cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size)
# Probabilities for generating words
probs = tf.nn.softmax(logits, name='probs')
# Loss function
cost = seq2seq.sequence_loss(
logits,
targets,
tf.ones([input_data_shape[0], input_data_shape[1]]))
# Optimizer
optimizer = tf.train.AdamOptimizer(lr)
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients]
train_op = optimizer.apply_gradients(capped_gradients)
"""
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"""
batches = get_batches(int_text, batch_size, seq_length)
with tf.Session(graph=train_graph) as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(num_epochs):
state = sess.run(initial_state, {input_text: batches[0][0]})
for batch_i, (x, y) in enumerate(batches):
feed = {
input_text: x,
targets: y,
initial_state: state,
lr: learning_rate}
train_loss, state, _ = sess.run([cost, final_state, train_op], feed)
# Show every <show_every_n_batches> batches
if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(
epoch_i,
batch_i,
len(batches),
train_loss))
# Save Model
saver = tf.train.Saver()
saver.save(sess, save_dir)
print('Model Trained and Saved')
Epoch 0 Batch 0/41 train_loss = 8.822 Epoch 2 Batch 18/41 train_loss = 6.089 Epoch 4 Batch 36/41 train_loss = 5.247 Epoch 7 Batch 13/41 train_loss = 5.075 Epoch 9 Batch 31/41 train_loss = 4.863 Epoch 12 Batch 8/41 train_loss = 4.820 Epoch 14 Batch 26/41 train_loss = 4.615 Epoch 17 Batch 3/41 train_loss = 4.412 Epoch 19 Batch 21/41 train_loss = 4.222 Epoch 21 Batch 39/41 train_loss = 4.117 Epoch 24 Batch 16/41 train_loss = 4.049 Epoch 26 Batch 34/41 train_loss = 3.809 Epoch 29 Batch 11/41 train_loss = 3.745 Epoch 31 Batch 29/41 train_loss = 3.505 Epoch 34 Batch 6/41 train_loss = 3.332 Epoch 36 Batch 24/41 train_loss = 3.275 Epoch 39 Batch 1/41 train_loss = 3.220 Epoch 41 Batch 19/41 train_loss = 3.069 Epoch 43 Batch 37/41 train_loss = 3.000 Epoch 46 Batch 14/41 train_loss = 2.994 Epoch 48 Batch 32/41 train_loss = 2.838 Epoch 51 Batch 9/41 train_loss = 2.931 Epoch 53 Batch 27/41 train_loss = 2.675 Epoch 56 Batch 4/41 train_loss = 2.689 Epoch 58 Batch 22/41 train_loss = 2.451 Epoch 60 Batch 40/41 train_loss = 2.551 Epoch 63 Batch 17/41 train_loss = 2.378 Epoch 65 Batch 35/41 train_loss = 2.441 Epoch 68 Batch 12/41 train_loss = 2.371 Epoch 70 Batch 30/41 train_loss = 2.336 Epoch 73 Batch 7/41 train_loss = 2.282 Epoch 75 Batch 25/41 train_loss = 2.212 Epoch 78 Batch 2/41 train_loss = 2.305 Epoch 80 Batch 20/41 train_loss = 2.160 Epoch 82 Batch 38/41 train_loss = 2.197 Epoch 85 Batch 15/41 train_loss = 2.155 Epoch 87 Batch 33/41 train_loss = 2.107 Epoch 90 Batch 10/41 train_loss = 2.100 Epoch 92 Batch 28/41 train_loss = 2.121 Epoch 95 Batch 5/41 train_loss = 2.044 Epoch 97 Batch 23/41 train_loss = 1.993 Epoch 100 Batch 0/41 train_loss = 2.008 Epoch 102 Batch 18/41 train_loss = 2.004 Epoch 104 Batch 36/41 train_loss = 1.945 Epoch 107 Batch 13/41 train_loss = 1.901 Epoch 109 Batch 31/41 train_loss = 1.974 Epoch 112 Batch 8/41 train_loss = 1.962 Epoch 114 Batch 26/41 train_loss = 1.962 Epoch 117 Batch 3/41 train_loss = 2.047 Epoch 119 Batch 21/41 train_loss = 1.925 Epoch 121 Batch 39/41 train_loss = 1.914 Epoch 124 Batch 16/41 train_loss = 1.910 Epoch 126 Batch 34/41 train_loss = 1.893 Epoch 129 Batch 11/41 train_loss = 1.902 Epoch 131 Batch 29/41 train_loss = 1.946 Epoch 134 Batch 6/41 train_loss = 1.803 Epoch 136 Batch 24/41 train_loss = 1.837 Epoch 139 Batch 1/41 train_loss = 1.873 Epoch 141 Batch 19/41 train_loss = 1.830 Epoch 143 Batch 37/41 train_loss = 1.796 Epoch 146 Batch 14/41 train_loss = 1.861 Epoch 148 Batch 32/41 train_loss = 1.841 Epoch 151 Batch 9/41 train_loss = 1.937 Epoch 153 Batch 27/41 train_loss = 1.866 Epoch 156 Batch 4/41 train_loss = 1.857 Epoch 158 Batch 22/41 train_loss = 1.793 Epoch 160 Batch 40/41 train_loss = 1.846 Epoch 163 Batch 17/41 train_loss = 1.802 Epoch 165 Batch 35/41 train_loss = 1.877 Epoch 168 Batch 12/41 train_loss = 1.742 Epoch 170 Batch 30/41 train_loss = 1.720 Epoch 173 Batch 7/41 train_loss = 1.677 Epoch 175 Batch 25/41 train_loss = 1.670 Epoch 178 Batch 2/41 train_loss = 1.838 Epoch 180 Batch 20/41 train_loss = 1.743 Epoch 182 Batch 38/41 train_loss = 1.840 Epoch 185 Batch 15/41 train_loss = 1.722 Epoch 187 Batch 33/41 train_loss = 1.727 Epoch 190 Batch 10/41 train_loss = 1.757 Epoch 192 Batch 28/41 train_loss = 1.718 Epoch 195 Batch 5/41 train_loss = 1.703 Epoch 197 Batch 23/41 train_loss = 1.720 Epoch 200 Batch 0/41 train_loss = 1.744 Epoch 202 Batch 18/41 train_loss = 1.748 Epoch 204 Batch 36/41 train_loss = 1.747 Epoch 207 Batch 13/41 train_loss = 1.681 Epoch 209 Batch 31/41 train_loss = 1.775 Epoch 212 Batch 8/41 train_loss = 1.743 Epoch 214 Batch 26/41 train_loss = 1.787 Epoch 217 Batch 3/41 train_loss = 1.812 Epoch 219 Batch 21/41 train_loss = 1.719 Epoch 221 Batch 39/41 train_loss = 1.795 Epoch 224 Batch 16/41 train_loss = 1.748 Epoch 226 Batch 34/41 train_loss = 1.711 Epoch 229 Batch 11/41 train_loss = 1.780 Epoch 231 Batch 29/41 train_loss = 1.757 Epoch 234 Batch 6/41 train_loss = 1.676 Epoch 236 Batch 24/41 train_loss = 1.746 Epoch 239 Batch 1/41 train_loss = 1.813 Epoch 241 Batch 19/41 train_loss = 1.749 Epoch 243 Batch 37/41 train_loss = 1.735 Epoch 246 Batch 14/41 train_loss = 1.772 Epoch 248 Batch 32/41 train_loss = 1.777 Epoch 251 Batch 9/41 train_loss = 1.770 Epoch 253 Batch 27/41 train_loss = 1.721 Epoch 256 Batch 4/41 train_loss = 1.807 Epoch 258 Batch 22/41 train_loss = 1.680 Epoch 260 Batch 40/41 train_loss = 1.709 Epoch 263 Batch 17/41 train_loss = 1.785 Epoch 265 Batch 35/41 train_loss = 1.756 Epoch 268 Batch 12/41 train_loss = 1.702 Epoch 270 Batch 30/41 train_loss = 1.722 Epoch 273 Batch 7/41 train_loss = 1.740 Epoch 275 Batch 25/41 train_loss = 1.686 Epoch 278 Batch 2/41 train_loss = 1.713 Epoch 280 Batch 20/41 train_loss = 1.641 Epoch 282 Batch 38/41 train_loss = 1.745 Epoch 285 Batch 15/41 train_loss = 1.751 Epoch 287 Batch 33/41 train_loss = 1.753 Epoch 290 Batch 10/41 train_loss = 1.763 Epoch 292 Batch 28/41 train_loss = 1.705 Epoch 295 Batch 5/41 train_loss = 1.751 Epoch 297 Batch 23/41 train_loss = 1.785 Model Trained and Saved
Save seq_length and save_dir for generating a new TV script.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import tensorflow as tf
import numpy as np
import helper
import problem_unittests as tests
_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
seq_length, load_dir = helper.load_params()
Get tensors from loaded_graph using the function get_tensor_by_name(). Get the tensors using the following names:
Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
def get_tensors(loaded_graph):
"""
Get input, initial state, final state, and probabilities tensor from <loaded_graph>
:param loaded_graph: TensorFlow graph loaded from file
:return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
"""
# TODO: Implement Function
InputTensor = loaded_graph.get_tensor_by_name("input:0")
InitialStateTensor = loaded_graph.get_tensor_by_name("initial_state:0")
FinalStateTensor = loaded_graph.get_tensor_by_name("final_state:0")
ProbsTensor = loaded_graph.get_tensor_by_name("probs:0")
return (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_tensors(get_tensors)
Tests Passed
Implement the pick_word() function to select the next word using probabilities.
def pick_word(probabilities, int_to_vocab):
"""
Pick the next word in the generated text
:param probabilities: Probabilites of the next word
:param int_to_vocab: Dictionary of word ids as the keys and words as the values
:return: String of the predicted word
"""
# TODO: Implement Function
print (probabilities)
print (int_to_vocab)
return np.random.choice(list(int_to_vocab.values()), p=probabilities)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_pick_word(pick_word)
[ 0.1 0.8 0.05 0.05]
{0: 'this', 1: 'is', 2: 'a', 3: 'test'}
Tests Passed
This will generate the TV script for you. Set gen_length to the length of TV script you want to generate.
gen_length = 100
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
loader = tf.train.import_meta_graph(load_dir + '.meta')
loader.restore(sess, load_dir)
# Get Tensors from loaded model
input_text, initial_state, final_state, probs = get_tensors(loaded_graph)
# Sentences generation setup
gen_sentences = [prime_word + ':']
prev_state = sess.run(initial_state, {input_text: np.array([[1]])})
# Generate sentences
for n in range(gen_length):
# Dynamic Input
dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
dyn_seq_length = len(dyn_input[0])
# Get Prediction
probabilities, prev_state = sess.run(
[probs, final_state],
{input_text: dyn_input, initial_state: prev_state})
pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)
gen_sentences.append(pred_word)
# Remove tokens
tv_script = ' '.join(gen_sentences)
for key, token in token_dict.items():
ending = ' ' if key in ['\n', '(', '"'] else ''
tv_script = tv_script.replace(' ' + token.lower(), key)
tv_script = tv_script.replace('\n ', '\n')
tv_script = tv_script.replace('( ', '(')
print(tv_script)
[ 1.19640853e-12 4.07364587e-06 8.56983234e-11 ..., 4.10233642e-13
2.04093311e-07 2.55578579e-11]
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8.40994971e-07 6.08635773e-08]
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[ 1.82230536e-16 5.84409040e-13 1.91639770e-19 ..., 9.24372801e-11
7.23015194e-14 2.68051411e-16]
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[ 9.97988045e-20 5.61336012e-11 1.65849794e-20 ..., 2.27681009e-22
5.32352101e-18 4.62564286e-21]
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[ 5.68898821e-29 5.81649294e-18 3.61292974e-28 ..., 5.11404182e-17
1.34767626e-20 2.09257623e-27]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 8.47498751e-24 6.90518032e-20 3.20749148e-24 ..., 6.28289881e-20
3.34822924e-23 6.17089046e-24]
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[ 1.27309052e-17 2.50093360e-17 5.53293551e-20 ..., 2.53994105e-16
3.83243945e-18 4.21513024e-21]
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9.35657664e-25 1.41371789e-29]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 3.52070465e-22 4.54687006e-06 4.16050884e-16 ..., 6.79732825e-20
2.72892095e-18 4.34354721e-21]
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[ 3.06861954e-21 1.48313098e-11 5.28729445e-21 ..., 3.43333632e-21
5.24112180e-23 1.04794363e-21]
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1.32918995e-10 6.72383383e-16]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 2.76029747e-24 2.03400259e-18 2.56646033e-16 ..., 1.05462033e-23
1.55132020e-15 2.46557725e-19]
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1.53369545e-13 3.99841792e-11]
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1.53019541e-15 1.52012583e-21]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 6.88983596e-26 1.28694011e-18 7.18839324e-24 ..., 6.74294823e-20
4.74974346e-20 1.00949033e-20]
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[ 3.93925733e-22 1.32580191e-16 6.97818026e-23 ..., 4.58826647e-25
2.69569745e-18 8.51708586e-18]
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1.07559462e-15 6.11222218e-27]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 5.08814280e-17 3.07594145e-10 4.08041347e-17 ..., 2.51589704e-11
3.21129762e-10 3.33247183e-13]
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[ 6.27302675e-18 1.65638868e-14 3.31646279e-19 ..., 2.90337138e-19
4.72796342e-14 3.71932539e-18]
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1.88158046e-16 8.79188965e-16]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 1.14590803e-24 1.24015891e-21 1.18243594e-25 ..., 2.57744178e-25
1.04101676e-22 1.86335459e-21]
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[ 8.24961559e-31 3.00774113e-26 2.14506441e-25 ..., 2.70982773e-24
1.53027527e-16 3.67400736e-31]
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[ 5.41954041e-15 1.57447289e-09 2.46816110e-16 ..., 3.15690345e-13
1.37399638e-07 3.62115476e-24]
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[ 1.42788085e-19 8.15541162e-16 2.07517270e-09 ..., 2.84429462e-14
1.56222203e-12 2.27364776e-20]
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[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
3.69728326e-32 0.00000000e+00]
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1.61299717e-12 5.82846586e-28]
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3.75579499e-21 1.55675603e-22]
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[ 7.84204861e-16 1.73635707e-11 2.05148420e-12 ..., 6.96588880e-12
2.82505654e-13 3.92728038e-12]
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'imaginary', 134: 'marry', 135: 'wondering', 136: 'indifference', 137: 'a-lug', 138: 'nards', 139: 'startup', 140: 'name', 141: 'grammys', 142: 'ding-a-ding-ding-ding-ding-ding-ding', 143: 'normals', 144: 'absolutely', 145: 'ralph_wiggum:', 146: 'inclination', 147: 'anyone', 148: 's', 149: 'tow', 150: 'week', 151: 'contest', 152: 'fad', 153: 'tablecloth', 154: 'owner', 155: 'tv_daughter:', 156: 'scrutinizing', 157: 'answering', 158: 'soul-crushing', 159: 'though:', 160: 'stein-stengel-', 161: 'mic', 162: 'and', 163: 'boys', 164: 'shares', 165: 'wish', 166: 'moonshine', 167: 's-a-u-r-c-e', 168: 'european', 169: 'devils:', 170: "what'd", 171: 'voted', 172: 'hate', 173: 'rapidly', 174: 'shores', 175: 'leak', 176: 'venom', 177: 'doy', 178: 'finding', 179: 'inflated', 180: 'je', 181: 'chuckles', 182: 'estranged', 183: 'ehhhhhhhh', 184: 'wipes', 185: 'rancid', 186: "wallet's", 187: 'football_announcer:', 188: 'wakede', 189: 'ultimate', 190: "cuckold's", 191: 'nucular', 192: 'shindig', 193: 'suspect', 194: 'ned', 195: 'woulda', 196: 'balls', 197: 'officials', 198: 'lose', 199: 'compadre', 200: 'annoying', 201: 'cauliflower', 202: 'uncreeped-out', 203: 'impatient', 204: 'wage', 205: 'stagehand:', 206: 'trench', 207: 'candidate', 208: 'murdoch', 209: 'geysir', 210: 'drummer', 211: 'script', 212: 'dignity', 213: 'gotcha', 214: 'joe', 215: "showin'", 216: 'fry', 217: 'oh', 218: 'hunger', 219: 'elocution', 220: 'diapers', 221: 'burn', 222: 'premise', 223: 'gulps', 224: "coffee'll", 225: "toot's", 226: 'ominous', 227: 'luxury', 228: 'burt', 229: 'watashi', 230: 'opens', 231: 'donation', 232: 'huggenkiss', 233: 'ivanna', 234: 'strategizing', 235: 'assert', 236: 'squeal', 237: 'playoff', 238: 'camera', 239: 'detective', 240: 'urge', 241: 'new_health_inspector:', 242: 'drag', 243: 'angry', 244: 'disaster', 245: 'eighty-six', 246: 'ingredient', 247: 'locked', 248: 'princesses', 249: "how're", 250: 'dreamy', 251: 'replace', 252: 'absentminded', 253: 'helpless', 254: 'conclusions', 255: 'once', 256: 'fires', 257: 'tourist', 258: 'skydiving', 259: 'office', 260: 'dory', 261: 'jerk', 262: 'drinks', 263: 'all-star', 264: "that's", 265: 'burps', 266: 'ungrateful', 267: 'intoxicants', 268: 'japanese', 269: 'lemonade', 270: 'laughter', 271: 'scent', 272: 'muffled', 273: 'sideshow_mel:', 274: 'unusually', 275: 'punishment', 276: 'career', 277: 'pressure', 278: 'thrust', 279: 'labor', 280: 'born', 281: 'marquee', 282: 'crack', 283: '1973', 284: 'awed', 285: 'barbed', 286: 'sugar', 287: 'this', 288: 'knocks', 289: "eatin'", 290: 'sticking', 291: "bashir's", 292: 'tracks', 293: "o'", 294: 'slender', 295: 'swig', 296: 'remains', 297: 'cavern', 298: 'lenny', 299: 'watt', 300: 'reaction', 301: 'er', 302: 'century', 303: 'tactful', 304: 'bag', 305: 'panties', 306: 'faceful', 307: 'because', 308: 'code', 309: 'appreciated', 310: 'tying', 311: 'cobra', 312: 'madonna', 313: 'ineffective', 314: 'believe', 315: 'calls', 316: 'henry', 317: 'blank', 318: 'film', 319: 'popping', 320: 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4.94355772e-12 1.30328997e-17]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 4.87319291e-22 3.67074230e-14 1.18194480e-15 ..., 3.19689823e-19
4.88024216e-15 1.53025247e-20]
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[ 3.33971757e-17 5.28185317e-14 6.01280161e-14 ..., 4.14107327e-17
1.32952398e-15 1.06336549e-17]
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2.68862805e-05 2.97617830e-28]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 1.32689399e-20 2.37157600e-15 3.47277078e-21 ..., 1.64309493e-21
1.06389831e-19 2.99692625e-21]
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[ 1.28691252e-10 1.94104086e-06 7.46055593e-05 ..., 3.79222077e-17
1.19521511e-11 4.04398086e-13]
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2.43129817e-15 9.30509426e-22]
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8.65342359e-16 1.33865046e-20]
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[ 5.85326745e-15 4.46714266e-09 3.74895766e-15 ..., 4.96311081e-09
1.73002305e-13 1.05703475e-11]
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[ 1.58604089e-19 1.26278732e-17 1.11555694e-20 ..., 1.23445351e-16
5.78615553e-20 2.86655305e-19]
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[ 3.41402278e-21 1.63054565e-08 6.62422714e-20 ..., 3.39412276e-04
1.36580454e-18 2.47296338e-26]
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[ 4.00998442e-15 1.77412633e-11 9.42319242e-20 ..., 2.58605446e-11
4.48601876e-12 3.03637142e-19]
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6.04539057e-19 1.80966035e-25]
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1.49271722e-13 1.26255149e-17]
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[ 4.30574639e-24 2.31313861e-12 1.83785118e-18 ..., 1.22407009e-21
2.50135152e-20 1.16021047e-26]
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2.46855137e-07 7.29493831e-13]
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[ 1.46108059e-25 3.47128495e-18 6.04051793e-21 ..., 3.63421587e-13
5.51264139e-21 7.75126080e-23]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 1.48436495e-24 5.65120971e-11 3.72349005e-23 ..., 8.43112624e-22
1.90665023e-27 2.75743095e-24]
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[ 1.19862484e-24 1.64328817e-18 3.81513405e-20 ..., 1.20730552e-19
2.54071540e-23 2.13645744e-28]
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'imaginary', 134: 'marry', 135: 'wondering', 136: 'indifference', 137: 'a-lug', 138: 'nards', 139: 'startup', 140: 'name', 141: 'grammys', 142: 'ding-a-ding-ding-ding-ding-ding-ding', 143: 'normals', 144: 'absolutely', 145: 'ralph_wiggum:', 146: 'inclination', 147: 'anyone', 148: 's', 149: 'tow', 150: 'week', 151: 'contest', 152: 'fad', 153: 'tablecloth', 154: 'owner', 155: 'tv_daughter:', 156: 'scrutinizing', 157: 'answering', 158: 'soul-crushing', 159: 'though:', 160: 'stein-stengel-', 161: 'mic', 162: 'and', 163: 'boys', 164: 'shares', 165: 'wish', 166: 'moonshine', 167: 's-a-u-r-c-e', 168: 'european', 169: 'devils:', 170: "what'd", 171: 'voted', 172: 'hate', 173: 'rapidly', 174: 'shores', 175: 'leak', 176: 'venom', 177: 'doy', 178: 'finding', 179: 'inflated', 180: 'je', 181: 'chuckles', 182: 'estranged', 183: 'ehhhhhhhh', 184: 'wipes', 185: 'rancid', 186: "wallet's", 187: 'football_announcer:', 188: 'wakede', 189: 'ultimate', 190: "cuckold's", 191: 'nucular', 192: 'shindig', 193: 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255: 'once', 256: 'fires', 257: 'tourist', 258: 'skydiving', 259: 'office', 260: 'dory', 261: 'jerk', 262: 'drinks', 263: 'all-star', 264: "that's", 265: 'burps', 266: 'ungrateful', 267: 'intoxicants', 268: 'japanese', 269: 'lemonade', 270: 'laughter', 271: 'scent', 272: 'muffled', 273: 'sideshow_mel:', 274: 'unusually', 275: 'punishment', 276: 'career', 277: 'pressure', 278: 'thrust', 279: 'labor', 280: 'born', 281: 'marquee', 282: 'crack', 283: '1973', 284: 'awed', 285: 'barbed', 286: 'sugar', 287: 'this', 288: 'knocks', 289: "eatin'", 290: 'sticking', 291: "bashir's", 292: 'tracks', 293: "o'", 294: 'slender', 295: 'swig', 296: 'remains', 297: 'cavern', 298: 'lenny', 299: 'watt', 300: 'reaction', 301: 'er', 302: 'century', 303: 'tactful', 304: 'bag', 305: 'panties', 306: 'faceful', 307: 'because', 308: 'code', 309: 'appreciated', 310: 'tying', 311: 'cobra', 312: 'madonna', 313: 'ineffective', 314: 'believe', 315: 'calls', 316: 'henry', 317: 'blank', 318: 'film', 319: 'popping', 320: 'liar', 321: 'cost', 322: 'meeting', 323: 'lis', 324: 'mock-up', 325: 'remembers', 326: 'wrestle', 327: "duff's", 328: 'product', 329: 'handshake', 330: 'pour', 331: 'predecessor', 332: 'blend', 333: 'ran', 334: 'around', 335: 'depressing', 336: 'sexton', 337: 'wheels', 338: 'helicopter', 339: 'able', 340: 'chosen', 341: 'half', 342: "barney's", 343: 'swooning', 344: 'examples', 345: 'undies', 346: 'nasty', 347: 'lib', 348: 'distraught', 349: 'darn', 350: 'move', 351: 'foibles', 352: 'punkin', 353: 'pushing', 354: 'mop', 355: 'little_man:', 356: 'mccarthy', 357: 'hosting', 358: 'refreshment', 359: "pullin'", 360: 'light', 361: 'dough', 362: 'uncle', 363: 'france', 364: 'stairs', 365: 'mediterranean', 366: 'was', 367: 'uglier', 368: 'pull', 369: 'a-a-b-b-a', 370: "leavin'", 371: 'soap', 372: 'grateful', 373: "usin'", 374: 'metal', 375: 'lindsay', 376: 'widow', 377: 'germans', 378: 'wrecking', 379: 'lenses', 380: 'lizard', 381: 'wants', 382: 'music', 383: "games'd", 384: "doin'", 385: 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[ 2.84781059e-18 1.34201931e-15 3.13105795e-21 ..., 2.25385003e-20
5.16690766e-22 1.29184897e-22]
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[ 5.79694474e-22 2.28774821e-09 1.99423636e-29 ..., 7.83139397e-19
7.04306731e-16 1.54360321e-29]
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[ 1.36146891e-25 1.98451360e-11 1.63240861e-24 ..., 1.64351068e-18
3.52144154e-17 2.86112112e-17]
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3.26915939e-17 4.46895452e-18]
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6.56491458e-11 2.42948439e-11]
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[ 4.47208266e-36 1.50068221e-29 6.79502178e-34 ..., 2.52548711e-24
7.26881876e-38 5.83020221e-29]
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5.13247223e-24 2.43986370e-25]
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[ 5.68830711e-22 4.87942064e-24 5.36387646e-19 ..., 6.11215707e-18
2.14103244e-17 5.22780546e-18]
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2.07969735e-22 1.42664878e-26]
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[ 8.03814878e-17 3.44916435e-12 2.22389513e-24 ..., 5.40849190e-24
1.17537675e-22 3.21965641e-16]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 1.31021269e-23 7.69710107e-20 2.29998070e-20 ..., 6.49722096e-30
6.99717702e-20 1.52356784e-12]
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[ 5.17184309e-25 1.34463282e-20 2.26737596e-29 ..., 5.23423615e-21
3.90444072e-14 5.60516365e-26]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 1.89066527e-25 1.48448655e-18 1.26390840e-22 ..., 7.48097057e-27
2.01039316e-17 3.06665358e-24]
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3.55236850e-20 1.19245179e-29]
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1.48340037e-24 1.00382802e-20]
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1.69934695e-17 1.35617323e-15]
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1.84328663e-23 7.53582714e-17]
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5.93700225e-20 5.62487171e-23]
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3.06817992e-16 9.59265281e-21]
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[ 2.26143748e-21 2.15700435e-08 1.01169440e-20 ..., 1.42933787e-09
2.17335473e-11 1.39301513e-11]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 3.96403787e-20 7.33308319e-17 7.61445846e-21 ..., 6.58162531e-21
2.50460760e-16 9.65270276e-16]
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4.75485791e-18 2.34102438e-22]
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[ 1.50209926e-25 2.30370380e-17 8.10357746e-21 ..., 4.59900447e-33
2.70551810e-15 3.74048798e-27]
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7.84679938e-14 2.15859658e-29]
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[ 8.64253090e-19 1.22985769e-12 7.54210101e-16 ..., 3.15163265e-21
4.06009568e-18 3.72710066e-12]
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1.70907147e-25 1.33718099e-37]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 8.61305561e-20 5.77690984e-20 1.55926738e-17 ..., 1.20524816e-18
1.88076271e-07 5.86137274e-20]
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[ 2.88973000e-27 7.50707368e-17 3.83760061e-26 ..., 2.37202328e-30
9.18437604e-10 6.88898699e-31]
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5.31762508e-21 4.97487673e-26]
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6761: 'saget', 6762: 'of', 6763: 'invulnerable', 6764: 'sassy', 6765: 'kicks', 6766: 'parrot', 6767: 'stained-glass', 6768: 'l', 6769: 'truck_driver:', 6770: 'verdict', 6771: 'all-american', 6772: 'wiping', 6773: 'waitress', 6774: 'generously', 6775: 'priority', 6776: 'speak', 6777: "smokin'_joe_frazier:", 6778: "pope's"}
[ 5.66473528e-15 1.14366232e-10 3.22861365e-17 ..., 1.63993526e-12
1.09691986e-11 4.98615791e-16]
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1.99678540e-20 9.64648451e-26]
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5.88995899e-18 2.75445784e-17]
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1.99926088e-26 5.85051988e-27]
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[ 1.00543272e-11 1.91642502e-09 4.67818602e-08 ..., 3.90451921e-10
1.97152988e-10 1.27540212e-09]
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1.39149864e-32 3.29872213e-35]
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